Cyclic Microchip Assay for Measurement of Hundreds of Functional Proteins in Single Neurons

Proteins are responsible for nearly all cell functions throughout cellular life. To date, the molecular functions of hundreds of proteins have been studied as they are critical to cellular processes. Those proteins are varied dramatically at different statuses and differential stages of the cells even in the same tissue. The existing single-cell tools can only analyze dozens of proteins and thus have not been able to fully characterize a cell yet. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology that affords the comprehensive functional proteome profiling of single cells. It permits multiple, separate rounds of multiplex assays of the same single cells on a microchip where each round detects 40-50 proteins. A decoding process is followed to assign protein identities and quantify protein detection signals. We demonstrate the technology on a neuron cell line by detecting 182 proteins that includes surface makers, neuron function proteins, neurodegeneration markers, signaling pathway proteins and transcription factors. Further study on 5XFAD mouse, an Alzheimer s Disease (AD) model, cells validate the utility of our technology which reveals the deep heterogeneity of brain cells. Through comparison with control mouse cells, the differentially expressed proteins in the AD mouse model have been detected. The single-cell CycMIST technology can potentially analyze the entire functional proteome spectrum, and thus it may offer new insights into cell machinery and advance many fields including systems biology, drug discovery, molecular diagnostics, and clinical studies.

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LTMG: A novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data

10.1101/430009 ◽

2018 ◽

Cited By ~ 1

Author(s):

Changlin Wan ◽

Wennan Chang ◽

Yu Zhang ◽

Fenil Shah ◽

Xiaoyu Lu ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Single Cells ◽

Cell Types ◽

R Package ◽

Data Sets ◽

Rna Seq ◽

Cell Functions ◽

Transcriptional Regulatory ◽

A Cell

ABSTRACTA key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a gene’s diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

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Characterization of CRISPR/Cas9 RANKL knockout mesenchymal stem cell clones based on single-cell printing technology and emulsion coupling assay as a low-cellularity workflow for single-cell cloning

10.1101/2020.08.17.253559 ◽

2020 ◽

Author(s):

Tobias Groß ◽

Csaba Jeney ◽

Darius Halm ◽

Günter Finkenzeller ◽

G. Björn Stark ◽

...

Keyword(s):

Single Cell ◽

Cell Line ◽

Large Scale ◽

Single Cells ◽

Protein Detection ◽

Cell Line Development ◽

Cell Printing ◽

Cell Clones ◽

The University

AbstractThe homogeneity of the genetically modified single-cells is a necessity for many applications such as cell line development, gene therapy, and tissue engineering and in particular for regenerative medical applications. The lack of tools to effectively isolate and characterize CRISPR/Cas9 engineered cells is considered as a significant bottleneck in these applications. Especially the incompatibility of protein detection technologies to confirm protein expression changes without a preconditional large-scale clonal expansion, creates a gridlock in many applications. To ameliorate the characterization of engineered cells, we propose an improved workflow, including single-cell printing/isolation technology based on fluorescent properties with high yield, a genomic edit screen (surveyor assay), mRNA rtPCR assessing altered gene expression and a versatile protein detection tool called emulsion-coupling to deliver a high-content, unified single-cell workflow. The workflow was exemplified by engineering and functionally validating RANKL knockout immortalized mesenchymal stem cells showing altered bone formation capacity of these cells. The resulting workflow is economical, without the requirement of large-scale clonal expansions of the cells with overall cloning efficiency above 30% of CRISPR/Cas9 edited cells. Nevertheless, as the single-cell clones are comprehensively characterized at an early, highly parallel phase of the development of cells including DNA, RNA, and protein levels, the workflow delivers a higher number of successfully edited cells for further characterization, lowering the chance of late failures in the development process.Author summaryI completed my undergraduate degree in biochemistry at the University of Ulm and finished my master's degree in pharmaceutical biotechnology at the University of Ulm and University of applied science of Biberach with a focus on biotechnology, toxicology and molecular biology. For my master thesis, I went to the University of Freiburg to the department of microsystems engineering, where I developed a novel workflow for cell line development. I stayed at the institute for my doctorate, but changed my scientific focus to the development of the emulsion coupling technology, which is a powerful tool for the quantitative and highly parallel measurement of protein and protein interactions. I am generally interested in being involved in the development of innovative molecular biological methods that can be used to gain new insights about biological issues. I am particularly curious to unravel the complex and often poorly understood protein interaction pathways that are the cornerstone of understanding cellular functionality and are a fundamental necessity to describe life mechanistically.

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Cell-to-cell diversification in ERBB-RAS-MAPK signal transduction that produces cell-type specific growth factor responses

10.1101/704429 ◽

2019 ◽

Author(s):

Hiraku Miyagi ◽

Michio Hiroshima ◽

Yasushi Sako

Keyword(s):

Growth Factors ◽

Single Cell ◽

Cell Fate ◽

Single Cells ◽

Bet Hedging ◽

Cell Type ◽

Cell Responses ◽

Single Cell Responses ◽

Specific Growth Factor ◽

A Cell

AbstractGrowth factors regulate cell fates, including their proliferation, differentiation, survival, and death, according to the cell type. Even when the response to a specific growth factor is deterministic for collective cell behavior, significant levels of fluctuation are often observed between single cells. Statistical analyses of single-cell responses provide insights into the mechanism of cell fate decisions but very little is known about the distributions of the internal states of cells responding to growth factors. Using multi-color immunofluorescent staining, we have here detected the phosphorylation of seven elements in the early response of the ERBB–RAS–MAPK system to two growth factors. Among these seven elements, five were analyzed simultaneously in distinct combinations in the same single cells. Although principle component analysis suggested cell-type and input specific phosphorylation patterns, cell-to-cell fluctuation was large. Mutual information analysis suggested that cells use multitrack (bush-like) signal transduction pathways under conditions in which clear cell fate changes have been reported. The clustering of single-cell response patterns indicated that the fate change in a cell population correlates with the large entropy of the response, suggesting a bet-hedging strategy is used in decision making. A comparison of true and randomized datasets further indicated that this large variation is not produced by simple reaction noise, but is defined by the properties of the signal-processing network.Author SummaryHow extracellular signals, such as growth factors (GFs), induce fate changes in biological cells is still not fully understood. Some GFs induce cell proliferation and others induce differentiation by stimulating a common reaction network. Although the response to each GF is reproducible for a cell population, not all single cells respond similarly. The question that arises is whether a certain GF conducts all the responding cells in the same direction during a fate change, or if it initially stimulates a variety of behaviors among single cells, from which the cells that move in the appropriate direction are later selected. Our current statistical analysis of single-cell responses suggests that the latter process, which is called a bet-hedging mechanism is plausible. The complex pathways of signal transmission seem to be responsible for this bet-hedging.

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Computational approaches towards reducing contamination in single-cell RNA-seq data

10.1101/2020.07.15.205062 ◽

2020 ◽

Author(s):

Siamak Yousefi ◽

Hao Chen ◽

Jesse F. Ingels ◽

Melinda S. McCarty ◽

Arthur G. Centeno ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Real Life ◽

Cell Types ◽

Cell Capture ◽

Rna Seq ◽

Sequence Analyses ◽

Cell Functions ◽

Biological Interpretation ◽

Different Cell Types

SUMMARYSingle cell RNA sequencing has enabled quantification of single cells and identification of different cell types and subtypes as well as cell functions in different tissues. Single cell RNA sequence analyses assume acquired RNAs correspond to cells, however, RNAs from contamination within the input data are also captured by these assays. The sequencing of background contamination as well as unwanted cells making their way to the final assay Potentially confound the correct biological interpretation of single cell transcriptomic data. Here we demonstrate two approaches to deal with background contamination as well as profiling of unwanted cells in the assays. We use three real-life datasets of whole-cell capture and nucleotide single-cell captures generated by Fluidigm and 10x technologies and show that these methods reduce the effect of contamination, strengthen clustering of cells and improves biological interpretation.

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Single cell network analysis with a mixture of Nested Effects Models

10.1101/258202 ◽

2018 ◽

Author(s):

Martin Pirkl ◽

Niko Beerenwinkel

Keyword(s):

Single Cell ◽

New Technologies ◽

Single Cells ◽

R Package ◽

Supplementary Information ◽

Data Sets ◽

Cell Network ◽

A Cell ◽

Supplementary Material ◽

Cell Data

AbstractMotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.ResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.AvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/[email protected], [email protected] informationSupplementary data are available.online.

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scTPA: A web tool for single-cell transcriptome analysis of pathway activation signatures

10.1101/2020.01.15.907592 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yan Zhang ◽

Yaru Zhang ◽

Jun Hu ◽

Ji Zhang ◽

Fangjie Guo ◽

...

Keyword(s):

Data Analysis ◽

Single Cell ◽

Single Cells ◽

Cell Types ◽

Biological Pathways ◽

Rna Seq ◽

Web Tool ◽

Functional Interpretation ◽

Cell Functions ◽

Pathway Activation

ABSTRACTThe most fundamental challenge in current single-cell RNA-seq data analysis is functional interpretation and annotation of cell clusters. The biological pathways in distinct cell types have different activation patterns, which facilitates understanding cell functions in single-cell transcriptomics. However, no effective web tool has been implemented for single-cell transcriptomic data analysis based on prior biological pathway knowledge. Here, we introduce scTPA (http://sctpa.bio-data.cn/sctpa), which is a web-based platform providing pathway-based analysis of single-cell RNA-seq data in human and mouse. scTPA incorporates four widely-used gene set enrichment methods to estimate the pathway activation scores of single cells based on a collection of available biological pathways with different functional and taxonomic classifications. The clustering analysis and cell-type-specific activation pathway identification were provided for the functional interpretation of cell types from pathway-oriented perspective. An intuitive interface allows users to conveniently visualize and download single-cell pathway signatures. Together, scTPA is a comprehensive tool to identify pathway activation signatures for dissecting single cell heterogeneity.

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An acoustic platform for single-cell, high-throughput measurements of the viscoelastic properties of cells

10.1101/2020.09.07.286898 ◽

2020 ◽

Author(s):

Valentin Romanov ◽

Giulia Silvani ◽

Huiyu Zhu ◽

Charles D Cox ◽

Boris Martinac

Keyword(s):

Mechanical Properties ◽

Single Cell ◽

High Throughput ◽

Single Cells ◽

Adherent Cells ◽

Dependent Manner ◽

Cellular Processes ◽

Membrane Cytoskeleton ◽

Change Temperature

ABSTRACTCellular processes including adhesion, migration and differentiation are governed by the distinct mechanical properties of each cell. Importantly, the mechanical properties of individual cells can vary depending on local physical and biochemical cues in a time-dependent manner resulting in significant inter-cell heterogeneity. While several different methods have been developed to interrogate the mechanical properties of single cells, throughput to capture this heterogeneity remains an issue. While new high-throughput techniques are slowly emerging, they are primarily aimed at characterizing cells in suspension, whereas high-throughput measurements of adherent cells have proven to be more challenging. Here, we demonstrate single-cell, high-throughput characterization of adherent cells using acoustic force spectroscopy. We demonstrate that cells undergo marked changes in viscoelasticity as a function of temperature, the measurements of which are facilitated by a closed microfluidic culturing environment that can rapidly change temperature between 21 °C and 37 °C. In addition, we show quantitative differences in cells exposed to different pharmacological treatments specifically targeting the membrane-cytoskeleton interface. Further, we utilize the high-throughput format of the AFS to rapidly probe, in excess of 1000 cells, three different cell-lines expressing different levels of a mechanosensitive protein, Piezo1, demonstrating the ability to differentiate between cells based on protein expression levels.

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Characterization of CRISPR/Cas9 RANKL knockout mesenchymal stem cell clones based on single-cell printing technology and Emulsion Coupling assay as a low-cellularity workflow for single-cell cloning

PLoS ONE ◽

10.1371/journal.pone.0238330 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0238330

Author(s):

Tobias Gross ◽

Csaba Jeney ◽

Darius Halm ◽

Günter Finkenzeller ◽

G. Björn Stark ◽

...

Keyword(s):

Single Cell ◽

Large Scale ◽

Single Cells ◽

Protein Detection ◽

High Yield ◽

Fluorescent Properties ◽

Cell Printing ◽

Protein Levels ◽

Cell Clones

The homogeneity of the genetically modified single-cells is a necessity for many applications such as cell line development, gene therapy, and tissue engineering and in particular for regenerative medical applications. The lack of tools to effectively isolate and characterize CRISPR/Cas9 engineered cells is considered as a significant bottleneck in these applications. Especially the incompatibility of protein detection technologies to confirm protein expression changes without a preconditional large-scale clonal expansion creates a gridlock in many applications. To ameliorate the characterization of engineered cells, we propose an improved workflow, including single-cell printing/isolation technology based on fluorescent properties with high yield, a genomic edit screen (Surveyor assay), mRNA RT-PCR assessing altered gene expression, and a versatile protein detection tool called emulsion-coupling to deliver a high-content, unified single-cell workflow. The workflow was exemplified by engineering and functionally validating RANKL knockout immortalized mesenchymal stem cells showing bone formation capacity of these cells. The resulting workflow is economical, without the requirement of large-scale clonal expansions of the cells with overall cloning efficiency above 30% of CRISPR/Cas9 edited cells. Nevertheless, as the single-cell clones are comprehensively characterized at an early, highly parallel phase of the development of cells including DNA, RNA, and protein levels, the workflow delivers a higher number of successfully edited cells for further characterization, lowering the chance of late failures in the development process.

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SIFTER: Electrophoretic complex fractionation protocol

10.21203/rs.3.pex-1582/v1 ◽

2021 ◽

Author(s):

Julea Vlassakis ◽

Louise L Hansen ◽

Amy E Herr

Keyword(s):

Single Cell ◽

Protein Interaction ◽

Gel Electrophoresis ◽

Protein Complexes ◽

Single Cells ◽

Polyacrylamide Gel Electrophoresis ◽

Simultaneous Detection ◽

Single Cell Protein ◽

Cell Protein ◽

A Cell

Abstract We introduce micro-arrayed, differential detergent fractionation for the simultaneous detection of protein complexes in 100s of individual cells with SIFTER (Single-cell protein Interaction Fractionation Through Electrophoresis and immunoassay Readout). Size-based fractionation of protein complexes is accomplished with five assay steps. First, a cell suspension generated by trypsinization is introduced onto a microwell array, and single cells are settled into the microwells by gravity. Cells are lysed in F-actin stabilization buffer that is delivered by a hydrogel lid. Second, the protein complexes are fractionated from the smaller monomers by polyacrylamide gel electrophoresis. Monomers are electrophoresed into the gel and are immobilized using a UV-induced covalent reaction to benzophenone. Third, a protein-complex depolymerization buffer is introduced by another hydrogel lid. Fourth, the recently depolymerized complexes are electrophoresed into a region of the gel separate from the immobilized monomers, where the complex fraction are in turn immobilized. Fifth, in-gel immunoprobing detects the immobilized populations of monomer and depolymerized complexes. These general steps are built on previously published protocols for bulk actin studies, single-cell western blotting, and bidirectional separations1-4.

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Cell explosions: single cell death triggers an avoidance response in local populations of the ecologically prominent phytoplankton genus Micromonas

10.1101/740605 ◽

2019 ◽

Author(s):

Richard Henshaw ◽

Jonathan Roberts ◽

Marco Polin

Keyword(s):

Cell Death ◽

Avoidance Response ◽

Single Cell ◽

Single Cells ◽

Local Environment ◽

Warning Signal ◽

Predator Prey ◽

A Cell ◽

Observed Behaviour ◽

Micromonas Pusilla

The global phytoplankton community, comprised of aquatic photosynthetic organisms, is acknowledged for being responsible for half of the global oxygen production Prominent among these is the pico-eukaryote Micromonas commoda (formally Micromonas pusilla of the genus Micromonas), which can be found in marine and coastal environments across the globe. Cell death of phytoplankton has been identified as contributing to the largest carbon transfers on the planet moving 109 tonnes of carbon in the oceans every day. During a cell death organic matter is released into the local environment which can act as both a food source and a warning signal for nearby organisms. Here we present a novel motility response to single cell death in populations of Micromonas sp., where the death of a single cell releases a chemical patch triggers surrounding cells to escape the immediate affected area. These so-called “burst events” are then modelled and compared with a spherically symmetric diffusing patch which is found to faithfully reproduce the observed behaviour. Finally, laser ablation of single cells reproduces the observed avoidance response, confirming that Micromonas sp. has evolved a specific motility response in order to escape harmful environments for example nearby predator-prey interactions or virus lysis induced cell death.

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